When a digital medical image is captured by a medical imaging machine using a particular imaging sensor, the captured image can often be distorted in one or more distance (i.e., length) dimensions across the image. That is, the distance relationship between any two points of anatomy in the image can be longer or shorter than the actual real-world distance. Such distortions of distance may be caused by the various parts of the imaging hardware including the imaging sensor, the x-ray generator, any filtering media, and the medical imaging machine itself.
Many times, a user (e.g., a medical doctor or dentist) of a medical imaging machine will switch imaging sensors during an imaging session in order to use different imaging sensors which are optimized for imaging different anatomical features of a patient. Also, a user may use two or more different medical imaging machines with several different imaging sensors to image various anatomical features of a patient.
Today, medical imaging software applications that are used to process captured digital medical images typically include a calibration routine to attempt to calibrate out any distance distortions in an image. Calibration may be performed as part of the capture process or may be performed post-capture. The calibration routine uses calibration data (e.g., a calibration factor) to calibrate the image for distance. However, such medical imaging software applications are typically limited to allowing only one calibration data (e.g., only one calibration factor) to be set for a given medical imaging machine (e.g., one calibration factor for each different manufacturer of a medical imaging machine type).
If a user of a particular medical imaging machine uses the medical imaging machine with a first imaging sensor (e.g. a first X-ray sensor), and then wants to proceed to use the same medical imaging machine with a second imaging sensor (for example a second X-ray sensor being of a different size than the first X-ray sensor), the user has to manually change the calibration factor in the medical imaging software application to correspond to the different distortions caused by the second imaging sensor. That is, the correct calibration factor for the combination of the medical imaging machine and the first imaging sensor is not the correct calibration factor for the combination of the medical imaging machine and the second imaging sensor.
Having to manually change calibration data when switching from one sensor to another for a given medical imaging machine wastes time and can lead to mistakes (e.g., forgetting to change the calibration data when switching sensors, or inputting the wrong calibration factor when switching sensors).
Further limitations and disadvantages of conventional, traditional, and proposed approaches will become apparent to one of skill in the art, through comparison of such systems and methods with the present invention as set forth in the remainder of the present application with reference to the drawings.